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农学学报 ›› 2024, Vol. 14 ›› Issue (4): 26-36.doi: 10.11923/j.issn.2095-4050.cjas2023-0092

• 生物技术 • 上一篇    下一篇

全基因组关联分析在玉米籽粒性状研究中的应用及其候选基因预测

陈昕怡(), 刘晨艳, 华明珠, 徐欣, 冯汶祥, 汪保华, 方辉()   

  1. 南通大学生命科学学院,江苏南通 226019
  • 收稿日期:2023-04-03 修回日期:2023-09-15 出版日期:2024-04-17 发布日期:2024-04-17
  • 通讯作者:
    方辉,男,1989年出生,山东威海人,讲师,博士,主要从事玉米功能基因组学研究。通信地址:226019 江苏省南通市崇川区啬园路9号 南通大学主校区纺化楼C1114,E-mail:
  • 作者简介:

    陈昕怡,女,2003年出生,江苏苏州人,本科,研究方向:玉米数量性状的遗传解析。通信地址:226019 江苏省南通市崇川区啬园路9号 南通大学主校区,E-mail:

  • 基金资助:
    国家自然科学基金项目“玉米籽粒油份基因ZmCtBP1的分子遗传机制研究”(32101730); 南通大学大学生创新训练计划项目“玉米籽粒发育基因ZmPPR6的克隆及功能分析”(2022098)

Grain-related Traits in Maize: Genome-wide Association Analysis and Candidate Gene Prediction

CHEN Xinyi(), LIU Chenyan, HUA Mingzhu, XU Xin, FENG Wenxiang, WANG Baohua, FANG Hui()   

  1. School of Life Sciences, Nantong University, Nantong 226019, Jiangsu, China
  • Received:2023-04-03 Revised:2023-09-15 Online:2024-04-17 Published:2024-04-17

摘要:

本研究旨在探索调控玉米籽粒发育的自然变异,以期为玉米产量性状的遗传改良提供科学依据。以150份遗传变异丰富的玉米自交系为材料进行研究。通过结合34342个SNP标记和3种模型,对5个籽粒相关性状进行全基因组关联分析。研究结果揭示了18个独立位点与目标性状显著关联,每个位点能够解释12.24%~15.41%的表型变异。同时,研究发现4对与籽粒长度相关的SNP之间存在显著的上位性互作,这些互作共能解释5.32%的表型变异。为了深入理解这些关联位点背后的分子机制,结合B73自交系籽粒发育的动态转录组数据和基因的功能注释,预测了19个候选基因,这些候选基因可以分为4类:6个酶、3个核糖体蛋白、1个转录因子和9个其他蛋白。这些候选基因的发现为解析玉米籽粒发育的分子机制以及改良籽粒大小和作物产量提供新的基因资源。通过本研究,我们不仅揭示了调控玉米籽粒发育的自然变异,还为玉米产量性状的遗传改良提供了新的基因资源。这些成果有望为玉米育种工作带来新的突破,提高玉米产量,从而更好地满足人类对粮食的需求。

关键词: 玉米, 籽粒大小, 产量, 全基因组关联分析, 候选基因预测

Abstract:

To explore the natural variations in regulating the maize kernel development and to assist in the genetic improvement of maize yield traits, in this study, 150 maize inbred lines with rich genetic variations were selected as materials for investigation. Combining 34,342 SNP markers and three models, a genome-wide association analysis was conducted on five grain-related traits. The results revealed that a total of 18 independent loci were significantly associated with the target traits, with each locus accounting for 12.24% to 15.41% of the phenotypic variations. Additionally, significant epistatic interactions were identified among four pairs of SNPs associated with kernel length, collectively explaining 5.32% of the phenotypic variations. By integrating the dynamic transcriptome data of kernel development in the B73 inbred line and functional annotations of genes, 19 candidate genes were predicted and classified into four categories: 6 enzymes, 3 ribosomal proteins, 1 transcription factor, and 9 other proteins. These candidate genes provide new genetic resources for deciphering the molecular mechanisms of maize kernel development and enhancing maize kernel size and yield. Through this research, we have not only uncovered the natural variations that regulate the development of corn kernels but also provided new genetic resources for the genetic improvement of corn yield traits. These findings are expected to bring new breakthroughs in corn breeding efforts, enhance corn production, and thereby better meet human needs for food.

Key words: maize, kernel size, yield, genome-wide association analysis, candidate genes prediction